Steve Tippins explains why small samples mislead: four heads in four coin flips happens by chance, but 3,500 heads in 4,000 flips signals a biased coin. Larger samples let random noise fade and reveal what's actually true. Use GPower to calculate the right size for your study.
When we're doing a quantitative study, we want to make sure we have enough data in order to produce results that have credibility. Have you ever flipped a coin four times and gotten heads four times in a row? It's not uncommon that that could happen. And then what's the next person going to guess? Oh, it's going to be tails? No, it's got a 50% chance. But if I've done it 4,000 times and I've got 3,500 heads, then maybe I've got something wrong with my coin. So your sample size should be big enough so that aberrations fall away. The G power test is the preferred test to determine sample size. Drop the word help in the comments and I'll send you a link to my free dissertation help community.
